DeepSeek-V3 vs DeepSeek-V3.1 vs Llama 4 Maverick 17B Instruct
Too close to call on our weighted score (Llama 4 Maverick 17B Instruct 58, DeepSeek-V3.1 55, DeepSeek-V3 50). The right pick depends on what you value most.
DeepSeekDeepSeek-V3
50/100- ECI132.3
- Price$0.32 / $1.10
- Context131K
- DeepSeek
DeepSeek-V3.1
55/100- ECI139.9
- Price$0.385 / $1.25
- Context131K
MetaLlama 4 Maverick 17B Instruct
58/100- ECI132.2
- Price$0.321 / $0.91
- Context1M
Too close to call
It is close. Our weighted score puts them within 2 points (Llama 4 Maverick 17B Instruct 58/100, DeepSeek-V3.1 55/100, DeepSeek-V3 50/100), so choose by what matters most for your work: DeepSeek-V3.1 for raw capability, Llama 4 Maverick 17B Instruct on price and Llama 4 Maverick 17B Instruct for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityDeepSeek-V3.1Capabilities Index (ECI): DeepSeek-V3.1 139.9 · DeepSeek-V3 132.3 · Llama 4 Maverick 17B Instruct 132.2
- Lowest priceLlama 4 Maverick 17B InstructLlama 4 Maverick 17B Instruct $0.468 · DeepSeek-V3 $0.515 · DeepSeek-V3.1 $0.601 per 1M tokens (3:1 blend)
- Longest contextLlama 4 Maverick 17B InstructLlama 4 Maverick 17B Instruct 1,000,000 · DeepSeek-V3 131,072 · DeepSeek-V3.1 131,072 tokens
- Widest inputsLlama 4 Maverick 17B InstructDeepSeek-V3: Text · DeepSeek-V3.1: Text · Llama 4 Maverick 17B Instruct: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | DeepSeek-V3 | DeepSeek-V3.1 | Llama 4 Maverick 17B Instruct |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 56 | 65 | 56 |
| Price | 25% | 64 | 60 | 66 |
| Inputs & features | 15% | 25 | 35 | 50 |
| Context window | 10% | 24 | 24 | 60 |
| Overall | 100% | 50/100 | 55/100 | 58/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | DeepSeek-V3DeepSeek | DeepSeek-V3.1DeepSeek | Llama 4 Maverick 17B InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 132.3 | 139.9 (best) | 132.2 |
| ECI rank | #121 of 148 | #100 of 148 (best) | #122 of 148 |
| GPQA DiamondGraduate-level science questions | 56.5% | — | 67.0% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 15.8% | — | 20.6% (best) |
| Price per million tokens | |||
| Input | $0.32 (best) | $0.385 | $0.321 |
| Output | $1.10 | $1.25 | $0.91 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.515 | $0.601 | $0.468 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 5 providers | Median of 8 providers | Median of 6 providers |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 1,000,000 tokens (best) |
| Max output | 8,192 tokens | 8,192 tokens | 16,384 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenDeepSeek Model License | OpenMIT License | Open |
| API model ID | — | — | — |
| API providers | 5 | 8 (best) | 6 |
| Released | Dec 26, 2024 | Aug 21, 2025 | Apr 5, 2025 |
| Knowledge cutoff | — | — | Aug 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- DeepSeek-V3$5.40
- DeepSeek-V3.1$6.35
- Llama 4 Maverick 17B Instruct$5.03
Which should you choose?
Which is better: DeepSeek-V3, DeepSeek-V3.1 or Llama 4 Maverick 17B Instruct?
It is close. Our weighted score puts them within 2 points (Llama 4 Maverick 17B Instruct 58/100, DeepSeek-V3.1 55/100, DeepSeek-V3 50/100), so choose by what matters most for your work: DeepSeek-V3.1 for raw capability, Llama 4 Maverick 17B Instruct on price and Llama 4 Maverick 17B Instruct for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek-V3, DeepSeek-V3.1 or Llama 4 Maverick 17B Instruct?
Llama 4 Maverick 17B Instruct is cheaper at $0.321 input / $0.91 output per million tokens (median across 6 API providers). DeepSeek-V3 costs $0.32 input / $1.10 output per million tokens (median across 5 API providers); DeepSeek-V3.1 costs $0.385 input / $1.25 output per million tokens (median across 8 API providers). At a typical mix of three input tokens to one output token, that is $0.468 per million tokens for Llama 4 Maverick 17B Instruct versus $0.515 for DeepSeek-V3 (1.1× as much) and $0.601 for DeepSeek-V3.1 (1.3× as much).
Which scores higher on benchmarks?
DeepSeek-V3.1 scores higher on the Capabilities Index (ECI): DeepSeek-V3.1 139.9 (#100 of 148), DeepSeek-V3 132.3 (#121 of 148) and Llama 4 Maverick 17B Instruct 132.2 (#122 of 148). Their confidence ranges do not overlap (136.1–143.3 vs 127.5–135.5), so the gap is a real one.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-V3, DeepSeek-V3.1 and Llama 4 Maverick 17B Instruct yet, so there is no like-for-like coding score. On overall capability, DeepSeek-V3.1 leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Llama 4 Maverick 17B Instruct has the largest context window at 1,000,000 tokens, against 131,072 for DeepSeek-V3 and 131,072 for DeepSeek-V3.1. Maximum output per response: DeepSeek-V3 up to 8,192, DeepSeek-V3.1 up to 8,192, Llama 4 Maverick 17B Instruct up to 16,384 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-V3 accepts text; DeepSeek-V3.1 accepts text; Llama 4 Maverick 17B Instruct accepts text and images. Llama 4 Maverick 17B Instruct handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (DeepSeek Model License and MIT License), so you can self-host them.
Which is newer?
DeepSeek-V3.1 is the newest, released Aug 21, 2025. Llama 4 Maverick 17B Instruct came out Apr 5, 2025; DeepSeek-V3 came out Dec 26, 2024. Knowledge cutoff: Llama 4 Maverick 17B Instruct Aug 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
